{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CW\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torch.autograd.gradcheck import zero_gradients\n",
    "import torch.utils.data.dataloader as Data\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "import numpy as np\n",
    "import cv2\n",
    "from tools import show_images_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "#获取计算设备 默认是CPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "#图像加载以及预处理\n",
    "image_path=\"../picture/cow.jpeg\"\n",
    "orig = cv2.imread(image_path)[..., ::-1]\n",
    "orig = cv2.resize(orig, (224, 224))\n",
    "img = orig.copy().astype(np.float32)\n",
    "\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "img /= 255.0\n",
    "img = (img - mean) / std\n",
    "img = img.transpose(2, 0, 1)\n",
    "\n",
    "img=np.expand_dims(img, axis=0)\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "#使用预测模式 主要影响droupout和BN层的行为\n",
    "model = models.alexnet(pretrained=True).to(device).eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#adam的最大迭代次数 论文中建议10000次 测试阶段1000也可以 1000次可以完成95%的优化工作\n",
    "max_iterations=1000\n",
    "#adam学习速率\n",
    "learning_rate=0.01\n",
    "#二分查找最大次数\n",
    "binary_search_steps=10\n",
    "#c的初始值\n",
    "initial_const=1e2\n",
    "confidence=initial_const\n",
    "\n",
    "#k值\n",
    "k=40\n",
    "\n",
    "#像素值区间\n",
    "boxmin = -3.0\n",
    "boxmax = 3.0\n",
    "\n",
    "#类别数 pytorch的实现里面是1000\n",
    "num_labels=1000\n",
    "\n",
    "#攻击目标标签 必须使用one hot编码\n",
    "target_label=288\n",
    "tlab=Variable(torch.from_numpy(np.eye(num_labels)[target_label]).to(device).float())\n",
    "\n",
    "\n",
    "print()\n",
    "\n",
    "shape = (1,3,224,224)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#c的初始化边界\n",
    "lower_bound = 0\n",
    "c=initial_const\n",
    "upper_bound = 1e10\n",
    "\n",
    "# the best l2, score, and image attack\n",
    "o_bestl2 = 1e10\n",
    "o_bestscore = -1\n",
    "o_bestattack = [np.zeros(shape)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o_bestl2=10000000000.0 confidence=100.0\n",
      "attack success l2=41.4673957824707 target_label=288\n",
      "iteration=100 loss=107.25787353515625 loss1=0.0 loss2=107.25787353515625\n",
      "iteration=200 loss=110.32181549072266 loss1=0.0 loss2=110.32181549072266\n",
      "iteration=300 loss=95.73548889160156 loss1=0.0 loss2=95.73548889160156\n",
      "iteration=400 loss=114.7618408203125 loss1=0.0 loss2=114.7618408203125\n",
      "iteration=500 loss=129.7127227783203 loss1=0.0 loss2=129.7127227783203\n",
      "iteration=600 loss=113.28498840332031 loss1=0.0 loss2=113.28498840332031\n",
      "iteration=700 loss=96.13314819335938 loss1=0.0 loss2=96.13314819335938\n",
      "iteration=800 loss=126.3227767944336 loss1=0.0 loss2=126.3227767944336\n",
      "iteration=900 loss=130.9044189453125 loss1=0.0 loss2=130.9044189453125\n",
      "iteration=1000 loss=110.85984802246094 loss1=0.0 loss2=110.85984802246094\n",
      "\n",
      "outer_step=0 confidence 100.0->50.0\n",
      "o_bestl2=41.4673957824707 confidence=50.0\n",
      "attack success l2=39.109554290771484 target_label=288\n",
      "iteration=100 loss=105.87308502197266 loss1=0.0 loss2=105.87308502197266\n",
      "iteration=200 loss=96.33572387695312 loss1=0.0 loss2=96.33572387695312\n",
      "iteration=300 loss=102.38907623291016 loss1=0.0 loss2=102.38907623291016\n",
      "iteration=400 loss=104.57801818847656 loss1=0.0 loss2=104.57801818847656\n",
      "iteration=500 loss=79.44497680664062 loss1=0.0 loss2=79.44497680664062\n",
      "iteration=600 loss=121.66686248779297 loss1=0.0 loss2=121.66686248779297\n",
      "iteration=700 loss=94.71481323242188 loss1=0.0 loss2=94.71481323242188\n",
      "iteration=800 loss=100.86616516113281 loss1=0.0 loss2=100.86616516113281\n",
      "iteration=900 loss=131.15611267089844 loss1=0.0 loss2=131.15611267089844\n",
      "iteration=1000 loss=103.30465698242188 loss1=0.0 loss2=103.30465698242188\n",
      "\n",
      "outer_step=1 confidence 50.0->25.0\n",
      "o_bestl2=39.109554290771484 confidence=25.0\n",
      "iteration=100 loss=100.46819305419922 loss1=0.0 loss2=100.46819305419922\n",
      "iteration=200 loss=93.9729995727539 loss1=8.788108825683594 loss2=85.18489074707031\n",
      "iteration=300 loss=85.76719665527344 loss1=0.0 loss2=85.76719665527344\n",
      "iteration=400 loss=93.00666046142578 loss1=0.0 loss2=93.00666046142578\n",
      "iteration=500 loss=90.5523910522461 loss1=0.0 loss2=90.5523910522461\n",
      "iteration=600 loss=84.93040466308594 loss1=0.0 loss2=84.93040466308594\n",
      "iteration=700 loss=86.22266387939453 loss1=0.0 loss2=86.22266387939453\n",
      "iteration=800 loss=107.3543472290039 loss1=0.0 loss2=107.3543472290039\n",
      "iteration=900 loss=79.44583129882812 loss1=0.0 loss2=79.44583129882812\n",
      "iteration=1000 loss=98.35907745361328 loss1=0.0 loss2=98.35907745361328\n",
      "\n",
      "outer_step=2 confidence 25.0->12.5\n",
      "o_bestl2=39.109554290771484 confidence=12.5\n",
      "attack success l2=35.433685302734375 target_label=288\n",
      "iteration=100 loss=90.58235931396484 loss1=0.0 loss2=90.58235931396484\n",
      "iteration=200 loss=77.17820739746094 loss1=0.0 loss2=77.17820739746094\n",
      "iteration=300 loss=75.82836151123047 loss1=0.0 loss2=75.82836151123047\n",
      "iteration=400 loss=67.53668975830078 loss1=0.0 loss2=67.53668975830078\n",
      "iteration=500 loss=71.25297546386719 loss1=0.0 loss2=71.25297546386719\n",
      "iteration=600 loss=78.9335708618164 loss1=0.0 loss2=78.9335708618164\n",
      "iteration=700 loss=68.40188598632812 loss1=1.3152599334716797 loss2=67.08662414550781\n",
      "iteration=800 loss=73.62308502197266 loss1=0.0 loss2=73.62308502197266\n",
      "iteration=900 loss=69.34520721435547 loss1=0.0 loss2=69.34520721435547\n",
      "iteration=1000 loss=78.09237670898438 loss1=0.0 loss2=78.09237670898438\n",
      "\n",
      "outer_step=3 confidence 12.5->6.25\n",
      "o_bestl2=35.433685302734375 confidence=6.25\n",
      "attack success l2=35.20536422729492 target_label=288\n",
      "iteration=100 loss=67.40435028076172 loss1=0.0 loss2=67.40435028076172\n",
      "iteration=200 loss=65.46063995361328 loss1=0.0 loss2=65.46063995361328\n",
      "iteration=300 loss=59.30821228027344 loss1=0.0 loss2=59.30821228027344\n",
      "iteration=400 loss=54.28768539428711 loss1=0.0 loss2=54.28768539428711\n",
      "iteration=500 loss=61.25852966308594 loss1=2.5685787200927734 loss2=58.68994903564453\n",
      "iteration=600 loss=60.63889694213867 loss1=0.0 loss2=60.63889694213867\n",
      "iteration=700 loss=55.33046340942383 loss1=0.0 loss2=55.33046340942383\n",
      "iteration=800 loss=54.62584686279297 loss1=2.67336368560791 loss2=51.952484130859375\n",
      "iteration=900 loss=59.454036712646484 loss1=0.0 loss2=59.454036712646484\n",
      "iteration=1000 loss=53.588008880615234 loss1=0.0 loss2=53.588008880615234\n",
      "\n",
      "outer_step=4 confidence 6.25->3.125\n",
      "o_bestl2=35.20536422729492 confidence=3.125\n",
      "attack success l2=31.411298751831055 target_label=288\n",
      "iteration=100 loss=49.9055290222168 loss1=0.0 loss2=49.9055290222168\n",
      "iteration=200 loss=44.632896423339844 loss1=0.0 loss2=44.632896423339844\n",
      "iteration=300 loss=44.9448356628418 loss1=3.2131075859069824 loss2=41.731727600097656\n",
      "iteration=400 loss=40.50004577636719 loss1=0.0 loss2=40.50004577636719\n",
      "iteration=500 loss=41.12866973876953 loss1=1.609206199645996 loss2=39.51946258544922\n",
      "iteration=600 loss=49.425193786621094 loss1=0.0 loss2=49.425193786621094\n",
      "iteration=700 loss=46.29950714111328 loss1=2.965080738067627 loss2=43.33442687988281\n",
      "iteration=800 loss=41.748592376708984 loss1=0.0 loss2=41.748592376708984\n",
      "iteration=900 loss=42.982093811035156 loss1=0.0 loss2=42.982093811035156\n",
      "iteration=1000 loss=38.54259490966797 loss1=0.0 loss2=38.54259490966797\n",
      "\n",
      "outer_step=5 confidence 3.125->1.5625\n",
      "o_bestl2=31.411298751831055 confidence=1.5625\n",
      "attack success l2=23.82769775390625 target_label=288\n",
      "attack success l2=23.755870819091797 target_label=288\n",
      "attack success l2=23.445287704467773 target_label=288\n",
      "attack success l2=22.982948303222656 target_label=288\n",
      "attack success l2=22.447132110595703 target_label=288\n",
      "attack success l2=21.849987030029297 target_label=288\n",
      "attack success l2=21.197702407836914 target_label=288\n",
      "attack success l2=20.535945892333984 target_label=288\n",
      "attack success l2=19.853870391845703 target_label=288\n",
      "attack success l2=19.3002872467041 target_label=288\n",
      "attack success l2=18.89867401123047 target_label=288\n",
      "attack success l2=18.54351806640625 target_label=288\n",
      "attack success l2=18.249589920043945 target_label=288\n",
      "attack success l2=18.042430877685547 target_label=288\n",
      "attack success l2=17.890687942504883 target_label=288\n",
      "attack success l2=17.794065475463867 target_label=288\n",
      "iteration=100 loss=38.831260681152344 loss1=0.0 loss2=38.831260681152344\n",
      "iteration=200 loss=35.28888702392578 loss1=0.0 loss2=35.28888702392578\n",
      "iteration=300 loss=35.00935363769531 loss1=1.5411198139190674 loss2=33.46823501586914\n",
      "iteration=400 loss=32.93063735961914 loss1=0.0 loss2=32.93063735961914\n",
      "iteration=500 loss=33.43764114379883 loss1=0.0 loss2=33.43764114379883\n",
      "iteration=600 loss=33.123226165771484 loss1=1.7809927463531494 loss2=31.342233657836914\n",
      "iteration=700 loss=34.57627868652344 loss1=0.0 loss2=34.57627868652344\n",
      "iteration=800 loss=33.5791015625 loss1=0.0 loss2=33.5791015625\n",
      "iteration=900 loss=31.423006057739258 loss1=0.0 loss2=31.423006057739258\n",
      "iteration=1000 loss=34.03218078613281 loss1=0.0 loss2=34.03218078613281\n",
      "\n",
      "outer_step=6 confidence 1.5625->0.78125\n",
      "o_bestl2=17.794065475463867 confidence=0.78125\n",
      "attack success l2=8.561098098754883 target_label=288\n",
      "iteration=100 loss=36.68828201293945 loss1=21.64082145690918 loss2=15.047460556030273\n",
      "iteration=200 loss=33.350807189941406 loss1=9.452007293701172 loss2=23.898801803588867\n",
      "iteration=300 loss=30.417694091796875 loss1=4.928907871246338 loss2=25.488786697387695\n",
      "iteration=400 loss=34.12792205810547 loss1=6.435489654541016 loss2=27.69243049621582\n",
      "iteration=500 loss=32.7559814453125 loss1=3.122875213623047 loss2=29.633106231689453\n",
      "iteration=600 loss=33.492393493652344 loss1=2.0950734615325928 loss2=31.397319793701172\n",
      "iteration=700 loss=30.96166229248047 loss1=0.0 loss2=30.96166229248047\n",
      "iteration=800 loss=32.08905792236328 loss1=0.4184901714324951 loss2=31.670568466186523\n",
      "iteration=900 loss=31.561355590820312 loss1=0.0 loss2=31.561355590820312\n",
      "iteration=1000 loss=32.68427276611328 loss1=1.463073492050171 loss2=31.221200942993164\n",
      "\n",
      "outer_step=7 confidence 0.78125->0.390625\n",
      "o_bestl2=8.561098098754883 confidence=0.390625\n",
      "attack success l2=6.353428840637207 target_label=288\n",
      "iteration=100 loss=22.197689056396484 loss1=15.588537216186523 loss2=6.609152317047119\n",
      "iteration=200 loss=21.93231201171875 loss1=14.070259094238281 loss2=7.862052917480469\n",
      "iteration=300 loss=21.874364852905273 loss1=14.178201675415039 loss2=7.696163177490234\n",
      "iteration=400 loss=21.483701705932617 loss1=13.330510139465332 loss2=8.153191566467285\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration=500 loss=21.66114044189453 loss1=13.844840049743652 loss2=7.816301345825195\n",
      "iteration=600 loss=21.207347869873047 loss1=13.445316314697266 loss2=7.762031078338623\n",
      "iteration=700 loss=21.472227096557617 loss1=13.986891746520996 loss2=7.485334873199463\n",
      "iteration=800 loss=21.07838249206543 loss1=13.512225151062012 loss2=7.566157817840576\n",
      "iteration=900 loss=21.337932586669922 loss1=13.817378997802734 loss2=7.520554542541504\n",
      "iteration=1000 loss=21.57647705078125 loss1=13.782630920410156 loss2=7.793846130371094\n",
      "\n",
      "outer_step=8 confidence 0.390625->0.1953125\n",
      "o_bestl2=6.353428840637207 confidence=0.1953125\n",
      "iteration=100 loss=11.907797813415527 loss1=9.742632865905762 loss2=2.1651651859283447\n",
      "iteration=200 loss=11.844062805175781 loss1=10.119562149047852 loss2=1.7245005369186401\n",
      "iteration=300 loss=11.848462104797363 loss1=10.304091453552246 loss2=1.5443702936172485\n",
      "iteration=400 loss=12.11383056640625 loss1=10.405445098876953 loss2=1.7083849906921387\n",
      "iteration=500 loss=11.796277046203613 loss1=10.072420120239258 loss2=1.7238565683364868\n",
      "iteration=600 loss=11.949525833129883 loss1=10.289531707763672 loss2=1.6599936485290527\n",
      "iteration=700 loss=12.059813499450684 loss1=9.568415641784668 loss2=2.4913980960845947\n",
      "iteration=800 loss=11.920845031738281 loss1=9.945411682128906 loss2=1.9754332304000854\n",
      "iteration=900 loss=11.857917785644531 loss1=9.916685104370117 loss2=1.9412328004837036\n",
      "iteration=1000 loss=11.809900283813477 loss1=10.067737579345703 loss2=1.742162823677063\n",
      "\n",
      "outer_step=9 confidence 0.1953125->0.09765625\n"
     ]
    }
   ],
   "source": [
    "# the resulting image, tanh'd to keep bounded from boxmin to boxmax\n",
    "boxmul = (boxmax - boxmin) / 2.\n",
    "boxplus = (boxmin + boxmax) / 2.\n",
    "\n",
    "for outer_step in range(binary_search_steps):\n",
    "    print(\"o_bestl2={} confidence={}\".format(o_bestl2,confidence)  )\n",
    "    \n",
    "    #把原始图像转换成图像数据和扰动的形态\n",
    "    timg = Variable(torch.from_numpy(np.arctanh((img - boxplus) / boxmul * 0.999999)).to(device).float())\n",
    "    modifier=Variable(torch.zeros_like(timg).to(device).float())\n",
    "    \n",
    "\n",
    "    #设置为不保存梯度值 自然也无法修改\n",
    "    #for param in model.parameters():\n",
    "    #    param.requires_grad = False\n",
    "        \n",
    "    #图像数据的扰动量梯度可以获取\n",
    "    modifier.requires_grad = True\n",
    "    \n",
    "\n",
    "    #定义优化器 仅优化modifier\n",
    "    optimizer = torch.optim.Adam([modifier],lr=learning_rate)\n",
    "    \n",
    "    for iteration in range(1,max_iterations+1):\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        #定义新输入\n",
    "        newimg = torch.tanh(modifier + timg) * boxmul + boxplus\n",
    "      \n",
    "        output=model(newimg)\n",
    "             \n",
    "        #定义cw中的损失函数\n",
    "        \n",
    "        #l2范数\n",
    "        #l2dist = tf.reduce_sum(tf.square(newimg-(tf.tanh(timg) * boxmul + boxplus)),[1,2,3])\n",
    "        #loss2 = tf.reduce_sum(l2dist)\n",
    "        loss2=torch.dist(newimg,(torch.tanh(timg) * boxmul + boxplus),p=2)\n",
    "        \n",
    "        \"\"\"\n",
    "        # compute the probability of the label class versus the maximum other\n",
    "            real = tf.reduce_sum((tlab)*output,1)\n",
    "            # 论文中的开源实现 other = tf.reduce_max((1-tlab)*output - (tlab*10000),1)\n",
    "            other = tf.reduce_max((1-tlab)*output)\n",
    "            loss1 = tf.maximum(0.0, other-real+k)\n",
    "            loss1 = tf.reduce_sum(const*loss1)\n",
    "        \"\"\"\n",
    "               \n",
    "        real=torch.max(output*tlab)\n",
    "        other=torch.max((1-tlab)*output)  \n",
    "        loss1=other-real+k   \n",
    "        loss1=torch.clamp(loss1,min=0)\n",
    "             \n",
    "        loss1=confidence*loss1\n",
    "           \n",
    "        loss=loss1+loss2\n",
    "        \n",
    "            \n",
    "        loss.backward(retain_graph=True)\n",
    "\n",
    "        optimizer.step()\n",
    "              \n",
    "        l2=loss2\n",
    "        \n",
    "        sc=output.data.cpu().numpy()\n",
    "        \n",
    "        # print out the losses every 10%\n",
    "        if iteration%(max_iterations//10) == 0:\n",
    "            print(\"iteration={} loss={} loss1={} loss2={}\".format(iteration,loss,loss1,loss2))\n",
    "              \n",
    "        if (l2 < o_bestl2) and (np.argmax(sc) == target_label  ):\n",
    "            print(\"attack success l2={} target_label={}\".format(l2,target_label))\n",
    "            o_bestl2 = l2\n",
    "            o_bestscore = np.argmax(sc)\n",
    "            o_bestattack = newimg.data.cpu().numpy()\n",
    "            \n",
    "    confidence_old=-1       \n",
    "    if (o_bestscore == target_label) and o_bestscore != -1:\n",
    "        #攻击成功 减小c\n",
    "        upper_bound = min(upper_bound,confidence)\n",
    "        if upper_bound < 1e9:\n",
    "                print()\n",
    "                confidence_old=confidence\n",
    "                confidence = (lower_bound + upper_bound)/2\n",
    "    else:\n",
    "        lower_bound = max(lower_bound,confidence)\n",
    "        confidence_old=confidence\n",
    "        if upper_bound < 1e9:\n",
    "                confidence = (lower_bound + upper_bound)/2\n",
    "        else:\n",
    "                confidence *= 10\n",
    "                \n",
    "    print(\"outer_step={} confidence {}->{}\".format(outer_step,confidence_old,confidence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n",
      "(1, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "print(o_bestattack.shape)\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 224, 224)\n",
      "l0=90441 l2=69825.06902252228\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adv=o_bestattack[0]\n",
    "print(adv.shape)\n",
    "adv = adv.transpose(1, 2, 0)\n",
    "adv = (adv * std) + mean\n",
    "adv = adv * 255.0\n",
    "adv = np.clip(adv, 0, 255).astype(np.uint8)\n",
    "\n",
    "show_images_diff(orig,0,adv,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
